A Dependence Maximization View of Clustering

2007

Conference Paper

ei

We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Independence Criterion (HSIC). Under this framework, we unify the geometric, spectral, and statistical dependence views of clustering, and subsume many existing algorithms as special cases (e.g. k-means and spectral clustering). Distinctive to our framework is that kernels can also be applied on the labels, which can endow them with particular structures. We also obtain a perturbation bound on the change in k-means clustering.

Author(s):

Song, L. and Smola, AJ. and Gretton, A. and Borgwardt, KM.

Journal:

Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007)

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